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All Journal IAES International Journal of Artificial Intelligence (IJ-AI) International Journal of Advances in Applied Sciences Jurnal Ilmu Komputer dan Informasi Jurnal Presipitasi : Media Komunikasi dan Pengembangan Teknik Lingkungan Jurnal Kesehatan Lingkungan indonesia Media Statistika JURNAL SISTEM INFORMASI BISNIS Jurnal Gaussian Jurnal Statistika Universitas Muhammadiyah Semarang Jurnal Sains dan Teknologi Jurnal Simetris TELKOMNIKA (Telecommunication Computing Electronics and Control) Jurnal Ilmiah Teknik Elektro Komputer dan Informatika (JITEKI) CommIT (Communication & Information Technology) Jurnal Ilmiah Kursor Jurnal Teknologi Informasi dan Ilmu Komputer Jurnal Transformatika JUITA : Jurnal Informatika WARTA Register: Jurnal Ilmiah Teknologi Sistem Informasi Journal of Information System E-Dimas: Jurnal Pengabdian kepada Masyarakat Sinkron : Jurnal dan Penelitian Teknik Informatika Jurnal RESTI (Rekayasa Sistem dan Teknologi Informasi) International Journal of Artificial Intelligence Research Jurnal Informatika INTENSIF: Jurnal Ilmiah Penelitian dan Penerapan Teknologi Sistem Informasi Seminar Nasional Variansi (Venue Artikulasi-Riset, Inovasi, Resonansi-Teori, dan Aplikasi Statistika) Jurnal Sisfokom (Sistem Informasi dan Komputer) ILKOM Jurnal Ilmiah KOMPUTIKA - Jurnal Sistem Komputer JTP - Jurnal Teknologi Pendidikan Indonesian Journal of Community Services Jurnal Pustaka Ilmiah Indonesian Journal of Electrical Engineering and Computer Science Journal of Applied Data Sciences Jurnal Riset Teknologi Pencegahan Pencemaran Industri Indonesian Journal of Librarianship J-KIP (Jurnal Keguruan dan Ilmu Pendidikan) Media Pustakawan STATISTIKA PROCEEDINGS OF THE INTERNATIONAL CONFERENCE ON DATA SCIENCE AND OFFICIAL STATISTICS Journal of Bioresources and Environmental Sciences Scientific Journal of Informatics Jurnal Informatika IJoICT (International Journal on Information and Communication Technology)
Budi Warsito
Departemen Statistika, Fakultas Sains Dan Matematika, Universitas Diponegoro
Agriculture, Biological Sciences & Forestry Humanities Chemical Engineering, Chemistry & Bioengineering Chemistry Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Earth & Planetary Sciences Economics, Econometrics & Finance Education Electrical & Electronics Engineering Energy Engineering Environmental Science Languange, Linguistic, Communication & Media Library & Information Science Materials Science & Nanotechnology Mathematics Physics Public Health Social Sciences Other

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Perbandingan Kinerja Inception- Resnetv2, Xception, Inception-v3, dan Resnet50 pada Gambar Bentuk Wajah Masruroh, Fitriana; Surarso, Bayu; Warsito, Budi
Jurnal Teknologi Informasi dan Ilmu Komputer Vol 10 No 1: Februari 2023
Publisher : Fakultas Ilmu Komputer, Universitas Brawijaya

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.25126/jtiik.2023104941

Abstract

Saat ini, klasifikasi bentuk wajah banyak diterapkan dalam berbagai bidang. Dalam bidang industri fashion dapat digunakan untuk pemilihan gaya rambut, pemilihan bingkai kacamata, tata rias, dan mode lainnya. Selain itu, dalam bidang medis bentuk wajah digunakan untuk bedah plastik. Identifikasi bentuk wajah adalah tugas yang menantang karena kompleksitas wajah, ukuran, pencahayaan, usia dan ekspresi. Banyak metode yang dikembangkan untuk memberikan hasil akurasi terbaik dalam klasifikasi bentuk wajah. Deep learning menjadi tren dibidang komputer vision karena memberikan hasil yang paling baik dari pada metode sebelumnya. Makalah ini mencoba menyajikan perbandingan kinerja klasifikasi wajah dengan empat arsitektur deep learning Xception, ResNet50, InceptionResNet-v2, Inception-v3. Dataset yang digunakan berjumlah 4500 gambar yang terbagi lima kelas heart, long, oblong, square, round. Berbagai pengoptimal deep learning diantaranya; transfer learning, optimizer deep learning, dropout dan fungsi aktivasi diterapkan untuk meningkatkan kinerja model. Perbandingan antara berbagai model CNN didasarkan kinerja metrik seperti accuracy, recall, precision dan F1-score. Dengan demikian dapat disimpulkan bahwa model Inception-ResNet-V2 menggunakan fungsi aktivasi Mish dan optimizer Nadam mencapai nilai tertinggi dengan accuracy dan f1-score masing-masing 92.00%, dan penggunaan waktu 65.0 menit. AbstractCurrently, face shape classification is widely applied in various fields. In the fashion industry, it can be used for hairstyle selection, eyeglass frame selection, makeup, and other modes. In the medical field, the face shape is used for plastic surgery. Identification of face shape is a challenging task due to the complexity of the face, size, lighting, age and expression. Many methods have been developed to provide the best accuracy results in the classification of face shapes. Deep learning is becoming a trend in the field of computer vision because it gives the best results than the previous method. This paper attempts to present a comparison of the performance of face classification with four deep learning architectures Xception, ResNet50, InceptionResNet-v2, Inception-v3. The dataset used is 4500 images divided into five classes heart, long, oblong, square, round. Various deep learning optimizers include; transfer learning, deep learning optimizer, dropout and activation functions are implemented to improve model performance. Comparisons between various CNN models are based on performance metrics such as accuracy, recall, precision and F1-score. Thus, it can be concluded that the Inception-ResNet-V2 model using the Mish activation function and the Nadam optimizer achieves the highest value with an accuracy and f1-score of 92.00%, and a time usage of 65.0 minutes. Thus, it can be concluded that the Inception-ResNet-V2 model using the Mish activation function and the Nadam optimizer achieves the highest value with an accuracy and f1-score of 92.00%, and a time usage of 65.0 minutes. 
Pengaruh Klasifikasi Sentimen Pada Ulasan Produk Amazon Berbasis Rekayasa Fitur dan K-Nearest Negihbor Putri, Nitami Lestari; Warsito, Budi; Surarso, Bayu
Jurnal Teknologi Informasi dan Ilmu Komputer Vol 11 No 1: Februari 2024
Publisher : Fakultas Ilmu Komputer, Universitas Brawijaya

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.25126/jtiik.20241117376

Abstract

Ulasan online menjadi faktor penting yang mendorong konsumen untuk membeli barang di e-commerce. Dalam e-commerce, ulasan pelanggan sebelumnya dapat membantu pembeli membuat keputusan yang lebih baik dengan memberikan informasi tentang kualitas produk, kekuatan dan kelemahan, perilaku penjual, harga, dan waktu pengiriman. Namun, keberadaan ulasan palsu menimbulkan tantangan dalam menilai sentimen yang diungkapkan oleh pelanggan asli secara benar. Dalam penelitian ini, berfokus pada analisis sentimen dan bertujuan untuk mengeksplorasi peran sentimen dalam ulasan produk Amazon. Penelitian ini menggunakan kombinasi fitur dari konten ulasan dengan menerapkan klasifikasi K-Nearest Neighbor untuk mengklasifikasikan polaritas sentimen ulasan secara akurat. Dalam mengekstrak skor polaritas dari ulasan, penelitian ini menggunakan pendekatan analisis sentimen berbasis leksikon yaitu Textblob Library dan menetapkan label sentimen dari ulasan produk. Hasil dari pemodelan yang diusulkan mencapai tingkat akurasi sebesar 83% yang menunjukkan keefektifan pemodelan yang diusulkan dalam analisis sentimen. Hasil dari penelitian ini dapat membantu konsumen dalam membuat keputusan pembelian dan membantu penjual dalam meningkatkan nilai produk dan layanan mereka berdasarkan feedback yang diberikan oleh pelanggan.
Enhancing Bank Financial Performance Assessment: A Literature Review of Deep Learning Applications Using the Kitchenham Method Ali, Mahrus; Gernowo, Rahmat; Warsito, Budi; Muthmainah, Faliha
Register: Jurnal Ilmiah Teknologi Sistem Informasi Vol 11 No 1 (2025): January
Publisher : Information Systems - Universitas Pesantren Tinggi Darul Ulum

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.26594/register.v11i1.4224

Abstract

The assessment of bank financial performance is crucial for ensuring the stability of the banking sector. With advancements in technology, especially deep learning (DL), there is increasing potential to improve the accuracy of risk prediction and financial performance evaluation in banks. However, challenges related to data imbalance and model complexity require more efficient approaches. This study aims to examine the application of DL in assessing bank financial performance, with a focus on credit risk, fraud detection, and bankruptcy prediction. A Systematic Literature Review (SLR) was conducted using the Kitchenham approach, analyzing 697 relevant articles to address nine research questions regarding the implementation of DL in the banking sector. This study contributes by providing insights into effective DL models that enhance financial performance and risk prediction in banks, while also offering recommendations for the development of more transparent models. The results indicate that models such as Long Short-Term Memory (LSTM) and Convolutional Neural Networks (CNN) perform well in handling large financial data. Additionally, hybrid models that combine DL with traditional models demonstrate higher accuracy in bankruptcy prediction and fraud detection.
Development of Customer Loyalty Measurement Application Using R Shiny with Structural Equation Model Partial Least Square Method, Customer Satisfaction Index, and Customer Loyalty Index Oktavia, Cintika; Warsito, Budi; Kadarrisman, Vincensius Gunawan Slamet
Jurnal Ilmiah Teknik Elektro Komputer dan Informatika Vol. 9 No. 4 (2023): December
Publisher : Universitas Ahmad Dahlan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.26555/jiteki.v9i4.26649

Abstract

One of Indonesia's well-known e-commerce platforms, Shopee, relies on information technology to run its business. The information technology used by Shopee is considered unable to meet customer satisfaction. Customer reviews are dissatisfied with the facilities provided by Shopee, and some customers compare Shopee with other e-commerce sites. The research contribution is the understanding that the proper use of information technology can positively impact customer experience, improve operational efficiency, and support business growth in the e-commerce industry. Research with a quantitative approach will build a website-based application as a statistical tool for data processing using R shiny so that the application results have high interactivity, dynamic visualization, and better explanation. The research will collect 100 data provided to customers who have transacted at Shopee and distributed through the telegram application, which is distributed to particular groups and channels for Shopee users. Data processing for this study will use the  Structural Equation Model Partial Least Square, Customer Satisfaction Index, Net Promoter Score, and Customer Loyalty Index. The study results show that electronic service quality and security seals positively and significantly affect customer satisfaction. Electronic service quality has a moderate effect on customer satisfaction, while electronic security seals have a slightly lower effect on customer satisfaction (t=5.584, p<0.001). Additionally, a significant correlation between customer loyalty and satisfaction was discovered (t=14.764, p=0.001). Research proves the need to improve service quality and security aspects to increase customer satisfaction on e-commerce platforms and the importance of maintaining customer satisfaction as a strategy to increase customer loyalty.
Assessing the impact of charcoal production activities on the Shea Nut tree vegetation cover Calvin, Esagu John; Warsito, Budi; Hidayat, Jafron Wasiq; Gertrude, Akello; Paul, Gudoyi M; Ahmed, Kamil
Journal of Bioresources and Environmental Sciences Vol 2, No 3 (2023): December 2023
Publisher : BIORE Scientia Publisher

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.14710/jbes.2023.19260

Abstract

Charcoal remains the main energy cooking source for urban dwellers in Uganda. The Shea Nut tree produces quality charcoal which is efficient and locally made. Therefore, it is facing increasing threats from the local communities so as to meet the mushrooming demand. The study analyses the state of the Shea Nut tree, drivers of charcoal production, predict Shea Nut tree vegetation coverage, and establish mechanisms for sustainable utilization and conservation of the Shea Nut trees in Kapelebyong District. Landsat images were classified using likelihood classification in ArcGIS and interviews were conducted whilst geospatial, Stata, and Nvivo tools were used for analysis. The findings reflect a sharp declining trend in the coverage of the shea Nut trees by 2.3% and 6% from 2002-2012 and 2012-2022 respectively. The major drivers include high demand from urban areas, the need for income, and unemployment. As a result, it is predicted that by 2032, the coverage will have reduced to only 713 hectares (7.3%) from 1277 hectares (10.6%) in 2022. Therefore, charcoal production with other land uses has greatly resulted in Shea Nut tree deterioration. The study recommends the use of alternative energy sources, the provision of alternative income-generating activities for the local communities, Government of Uganda through NFA needs to enforce the ways through which Shea Nut trees are managed and utilized in order to minimize illegal cutting.
Evaluation of Waste Transportation Routes in Salatiga City Haritsa, Rifda Tsaqifarani; Maryono, Maryono; Rahadian, Rully; Hermawan, Ferry; Warsito, Budi
Jurnal Riset Teknologi Pencegahan Pencemaran Industri Vol. 16 No. 1 (2025): May
Publisher : Balai Besar Standardisasi dan Pelayanan Jasa Pencegahan Pencemaran Industri

Show Abstract | Download Original | Original Source | Check in Google Scholar

Abstract

The problem of waste transportation is a major challenge in waste management in Salatiga City. With the amount of daily waste generated reaching 457.81 m³ and the volume transported only around 327.33 m³, the level of waste transportation has only reached 71.72%. This study aims to evaluate and optimize the current waste transportation route through a spatial approach using QGIS software. The methods used include field observation, primary and secondary data collection, and spatial analysis of the distribution of routes and workloads of the transport fleet consisting of 9 arm roll units and 1 dump truck unit, with a total average daily trip of 58 trips. The results of the comparison between the existing route and the planned route show a daily route length efficiency of 10.57 km (1.15%), fuel consumption savings of 2.73 liters per day, and travel time efficiency of 25 minutes. The volume of transported waste also increased from 83,730 kg/day to 89,500 kg/day (up 6.89%), which was achieved through more optimal route planning, additional trips to TPS Boja and Tingkir, and equalizing the workload between drivers. The results of this study confirm that GIS-based route optimization can increase the efficiency of distance, fuel, time, and productivity of the waste transportation system as a whole in Salatiga City.
UTAUT-2, HOT-Fit, and PLS-SEM for User Acceptance and Success of the Face Recognition Feature in CAT BKN Application Sari, Juwita Dwinda; Warsito, Budi; Wibowo, Catur Edi
Scientific Journal of Informatics Vol. 12 No. 4: November 2025
Publisher : Universitas Negeri Semarang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.15294/sji.v12i4.31229

Abstract

Purpose: Face recognition feature was implemented in the National Civil Service Agency's Computer-Assisted Test in 2021. There has been no evaluation of the system's acceptance and success. This study aims to measure user acceptance and evaluate the feature's success using the R Shiny application. Methods: The study utilized 337 respondents from a Google Form-based questionnaire distributed throughout the Regional Office VII of the National Civil Service Agency in Palembang. The hybrid model used was UTAUT-2 and HOT-Fit, with PLS-SEM statistical analysis. Acceptance analysis and feature evaluation were conducted using the developed R Shiny Dashboard. Results: The findings indicated that 15 of the 26 hypotheses were accepted. Behavioral intention and use behavior significantly influence hedonic motivation and habit. User behavior significantly influences user satisfaction, system quality, service quality, information quality, system use, and organizational structure and environment. As users become more familiar with the technology, their experience improves, and system utilization becomes more effective. Novelty: The integration of UTAUT-2 and HOT-Fit models within an R Shiny Dashboard was applied to analyze user acceptance and evaluate the face recognition feature in Computer Computer-Assisted Test selection process. The findings provide recommendations for feature development and improving participant face recognition performance. Moreover, the R Shiny Dashboard can be adapted for user experience analysis and system evaluation in other contexts.
Unstacking the Stack: Synthesis of Optimization Strategies for Stacked Ensemble Models in Multi-Domain Contexts Widiyatmoko, Carolus Borromeus; Gernowo, Rahmat; Warsito, Budi
Jurnal Sisfokom (Sistem Informasi dan Komputer) Vol. 15 No. 01 (2026): JANUARY
Publisher : ISB Atma Luhur

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.32736/sisfokom.v15i01.2545

Abstract

Stacked ensemble models (SEMs) remain widely used for integrating multiple learning algorithms into a single predictive system. However, SEMs continue to face challenges such as accuracy limitations, overfitting, high computational expenses, and limited interpretability. This study conducts a systematic review of 269 peer-reviewed papers published between 2020 and 2025, following the PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-Analyses) methodology to ensure transparency and rigor in article selection. The review identifies key technical issues in SEM implementations and synthesizes their corresponding optimization strategies. To address these challenges, a method-engineering-based modular three-stage framework is proposed, consisting of pre-processing, processing, and post-processing phases. Each stage targets specific weaknesses by improving data quality, optimizing models and hyperparameters, and enhancing interpretability and adaptability. The framework provides a structured foundation that links SEM optimization approaches with their development stages, supporting the design of robust, efficient, and interpretable ensemble models for practical applications.
Integration of UTAUT 2 and Delone & McLean to Evaluate Acceptance of Video Conference Application Bayastura, Shahnilna Fitrasha; Warsito, Budi; Nugraheni, Dinar Mutiara Kusumo
INTENSIF: Jurnal Ilmiah Penelitian dan Penerapan Teknologi Sistem Informasi Vol 6 No 2 (2022): August 2022
Publisher : Universitas Nusantara PGRI Kediri

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.29407/intensif.v6i2.17897

Abstract

This article explores how college students adopt video conferencing software for distance education. This research aims to examine the factors that influence the spread of video conferencing programs in Indonesia. A video conferencing application is a multimedia program that generates audio and visual content to facilitate real-time, two-way communication between its users. Because of COVID-19, classes of all kinds are now being taken online. As a result, more people are turning to tools like video conferencing. Therefore, learning how to access student video conferencing software is crucial. The UTAUT 2 and Delone & McLean models will be integrated into the analysis. A total of 327 people answered the survey. Next, we used the PLS-SEM technique in smart pls 3.0 to analyze the data collected from the respondents. The R-Square value of 26.2% for the retention intent variable and 62.3% for the user satisfaction variable demonstrate that independent variables in the study can explain endogenous variables and that the remaining variance is influenced by factors external to the survey.
A hybrid divisive K-means framework for big data–driven poverty analysis in Central Java Province Winarno, Bowo; Warsito, Budi; Surarso, Bayu
Indonesian Journal of Electrical Engineering and Computer Science Vol 41, No 1: January 2026
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v41.i1.pp258-269

Abstract

Clustering is essential in big data analytics, especially for partitioning high dimensional socioeconomic datasets to support interpretation and policy decisions. While K-Means is widely used for its simplicity and scalability, its strong sensitivity to initial centroid selection often leads to unstable results and slower convergence. Previous hybrid approaches, such as Agglomerative–K-Means, attempted to address this issue by using hierarchical clustering for centroid initialization; however, these methods rely on bottom-up merging, which can produce suboptimal initial partitions and increase computational overhead for larger datasets. To overcome these limitations, this study proposes a hybrid divisive–K-Means (DHC) model that employs top-down hierarchical splitting to generate more coherent initial centroids before refinement with K-Means. Using a multidimensional poverty dataset from Central Java Province provided by the Indonesian Central Bureau of Statistics (BPS), the performance of DHC was evaluated against standard K-Means and Agglomerative–K-Means. The assessment included execution time, convergence iterations, and cluster validity indices (Silhouette, Davies–Bouldin, and Calinski–Harabasz). Experimental results demonstrate that DHC reduces execution time by up to 97% and requires 40% fewer iterations than standard K-Means, while achieving comparable or improved cluster quality (e.g., CH Index increasing from 14.3 to 15.8). These findings indicate that the DHC model offers a more efficient and stable clustering solution, addressing the shortcomings of previous standard K-Means methods and improving performance for large-scale socioeconomic data analysis.
Co-Authors . Widayat Abdul Hoyyi Adi Waridi Basyirudin Arifin Adi Wibowo Adi Wibowo Agus Pamuji Agus Pamuji Agus Rusgiyono Agus Winarno, Agus Ahmad Lubis Ghozali Ahmed, Kamil Alan Prahutama Anindita Nur Safira Arafa Rahman Aziz Arbella Maharani Putri Arief Rachman Hakim Arief Rachman Hakim Arief Rachman Hakim Aries Susanty Aries Susanty Aris Sugiharto Arsyil Hendra Saputra Atmaja, Dinul Darma Atur Ekharisma Dewi Aurum Anisa Salsabela Bagus Dwi Saputra Bayastura, Shahnilna Fitrasha Bayu Surarso Bimastyaji Surya Ramadhan Budiyono Budiyono Calvin, Esagu John Catur Edi Widodo Chrisna Suhendi Cintika Oktavia Di Asih I Maruddani Di Mokhammad Hakim Ilmawan Dian Mariana L Manullang Dinar Mutiara Kusumo Nugraheni Dwi Ispriyanti Dyna Marisa Khairina Eka Rahmawati eka rahmawati Ekky Rosita Singgih Wigati Endang Fatmawati Endang Fatmawati Fachry Abda El Rahman Fadhilah, Husni Fadli Dony Pradana Faisal Fikri Utama Faliha Muthmainah Faridah, Hasna Fath Ezzati Kavabilla Fatiya Nur Umma Ferry Hermawan Fiqria Devi Ariyani Firdonsyah, Arizona Gayuh Kresnawati Gertrude, Akello Ghifar Rahman Gregorius Anung Hanindito Handayani, Sri Hanif Kusumasasmita Haritsa, Rifda Tsaqifarani Harjum Muharam Hasbi Yasin Hendri Setyawan Henny Widayanti, Henny Heriyanto Hizkia Christian Putra Setiadi Indra Jaya Infan Nur Kharismawan Intan Monica Hanmastiana Jafron Wasiq Hidayat Junta Zeniarja Juwanda, Farikhin Kadarrisman, Vincensius Gunawan Slamet Kiswanto Kiswanto M. Afif Amirillah M. Andang Novianta Maharani, Chintya Ayu Mahrus Ali Maori, Nadia Annisa Maryono Maryono Maryono Maryono Masruroh, Fitriana Maulida Najwa, Maulida Mifta Ardianti Moch. Abdul Mukid Mochamad Arief Budihardjo Mochammad Agung Wibowo Moh Ali Fikri mohamad jamil muhammad shodiq Muliyadi Muliyadi Munji Hanafi Mustafid Mustafid Mustaqim Mustaqim, Mustaqim Nani Ratnaningsih Nisa Afida Izati Noor Azizah Nur Fitriyah Nurcahyanti, Tri Meida Nurul Fajrin Aghentika Nurul Hidayati Oktavia, Cintika Oky Dwi Nurhayati Pandu Anggara Paul, Gudoyi M Perdana, Ery Purwanto Purwanto Puspita Kartikasari Puspita Kartikasari Puspitasari, Norma Putri, Nitami Lestari R Rizal Isnanto R. Rizal Isnanto RACHMAN HAKIM, ARIEF Rachmat Gernowo Rachmat Gernowo Rahmat Gernowo Rahmatul Akbar Ratna Kencana Putri Rini Nuraini Rita Rahmawati Rita Rahmawati Riva Amrulloh Riza Rizqi Robbi Arisandi Royani, Noorhanida Rukun Santoso Rully Rahadian Safitri, Adila Salma Farah Aliyah Sang Nur Cahya Widiutama Sari, Juwita Dwinda Silvia Elsa Suryana Siti Fadhilla Femadiyanti Sri Endah Moelya Artha Sri Sumiyati Sudarno Sudarno Sudarno Sudarno Sudarno utomo Sugito Sugito Sulardjaka Sulardjaka Suparti Suparti Suwardi, Dede Syafrudin Syafrudin Tarno Tarno Tarno Tarno Tatik Widiharih Tatik Widiharih Ta’fif Lukman Afandi Tri Yani Elisabeth Nababan Ummayah, Putri Qodar Vincensius Gunawan Slamet Kadarrisman Wahyul Amien Syafei Whisnumurti Adhiwibowo Wibowo, Catur Edi Widiyatmoko, Carolus Borromeus Winahyu Handayani Winarno, Bowo Yanuar Yoga Prasetyawan Yundari, Yundari